{"id":"W2242337013","doi":"","title":"Using CFS Data to Guide Regional Transportation Policy and Investment","year":2006,"lang":"en","type":"article","venue":"Transportation research circular","topic":"Transportation Systems and Infrastructure","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Context (archaeology); Commodity; Investment (military); Business; Order (exchange); Data collection; Set (abstract data type); Transport engineering; Finance; Computer science; Engineering; Geography; Political science; Politics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000864888,0.0002260052,0.0002444219,0.0008356518,0.0003698853,0.0002618037,0.0004028638,0.0001174287,0.0001311046],"category_scores_gemma":[0.00003422533,0.0002297759,0.00005806367,0.001406382,0.0001253821,0.001552694,0.000008619548,0.0002157651,0.00004694865],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008703915,"about_ca_system_score_gemma":0.0001919666,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0394488,"about_ca_topic_score_gemma":0.007410836,"domain_scores_codex":[0.9969828,0.00002884158,0.0006610524,0.0006823409,0.001145498,0.0004995078],"domain_scores_gemma":[0.9986532,0.00003793351,0.0001466351,0.0005479419,0.0005469783,0.00006731565],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001314169,0.0001808934,0.2150738,0.001031129,0.0001060045,0.0001493851,0.0008691791,0.01288533,0.04011246,0.6985961,0.02940535,0.001458952],"study_design_scores_gemma":[0.0005751151,0.000009534078,0.6742777,0.00006952754,0.00003782902,0.000001150846,0.0003589683,0.00115678,0.00006049905,0.005467651,0.3177393,0.000245969],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9848193,0.0002162942,0.009563564,0.00194877,0.000115535,0.0009739724,0.0002186937,0.0001382724,0.002005582],"genre_scores_gemma":[0.9909646,0.00001119453,0.002482449,0.00179242,0.001191542,0.00004168879,0.003263685,0.00005206153,0.0002003386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6931285,"threshold_uncertainty_score":0.9669476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1567368972678818,"score_gpt":0.3708562893813065,"score_spread":0.2141193921134247,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}